With the rise of social media and online news sources, fake news has become a significant issue globally. However, the detection of fake news in low resource languages like Bengali has received limited attention in research. In this paper, we propose a methodology consisting of four distinct approaches to classify fake news articles in Bengali using summarization and augmentation techniques with five pre-trained language models. Our approach includes translating English news articles and using augmentation techniques to curb the deficit of fake news articles. Our research also focused on summarizing the news to tackle the token length limitation of BERT based models. Through extensive experimentation and rigorous evaluation, we show the effectiveness of summarization and augmentation in the case of Bengali fake news detection. We evaluated our models using three separate test datasets. The BanglaBERT Base model, when combined with augmentation techniques, achieved an impressive accuracy of 96% on the first test dataset. On the second test dataset, the BanglaBERT model, trained with summarized augmented news articles achieved 97% accuracy. Lastly, the mBERT Base model achieved an accuracy of 86% on the third test dataset which was reserved for generalization performance evaluation. The datasets and implementations are available at https://github.com/arman-sakif/Bengali-Fake-News-Detection

翻译：随着社交媒体和在线新闻来源的兴起，假新闻已成为全球性的重大问题。然而，针对孟加拉语等低资源语言的假新闻检测研究仍十分有限。本文提出了一种包含四种不同方法的方法论，通过结合摘要技术和数据增强技术，利用五种预训练语言模型对孟加拉语假新闻文章进行分类。我们的方法包括翻译英语新闻文章，并运用数据增强技术来缓解假新闻文章数据不足的问题。同时，研究重点在于对新闻进行摘要处理，以解决基于BERT的模型存在的词元长度限制问题。通过大量实验和严格评估，我们证明了摘要技术和数据增强技术在孟加拉语假新闻检测中的有效性。我们使用三个独立的测试数据集对模型进行了评估。当结合数据增强技术时，BanglaBERT Base模型在第一个测试数据集上达到了96%的准确率。在第二个测试数据集上，使用摘要增强新闻文章训练的BanglaBERT模型取得了97%的准确率。最后，在用于泛化性能评估的第三个测试数据集上，mBERT Base模型达到了86%的准确率。数据集和实现代码可在https://github.com/arman-sakif/Bengali-Fake-News-Detection获取。